{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Variogram Calculation and Modeling Demonstration\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive workflow for calculating directional experimental variograms in 2D. \n",
    "\n",
    "* setting the variogram calculation parameters for identifying spatial data pairs \n",
    "\n",
    "This approach is essential for quantifying spatial continuity with sparsely sampled, irregular spatial data.\n",
    "\n",
    "I have more comprehensive workflows for variogram calculation:\n",
    "\n",
    "* [Experimental Variogram Calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n",
    "\n",
    "* [Determination of Major and Minor Spatial Continuity Directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "The variogram calculation parameters include:\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n",
    "\n",
    "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n",
    "\n",
    "* **bandwidth** is the maximum offset allowable from the lag vector \n",
    "\n",
    "\n",
    "#### Variogram Modeling\n",
    "\n",
    "Spatial continuity can be modeled with nested, positive definate variogram structures:\n",
    "\n",
    "\\begin{equation}\n",
    "\\Gamma_x(\\bf{h}) = \\sum_{i=1}^{nst} \\gamma_i(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "where $\\Gamma_x(\\bf{h})$ is the nested variogram model resulting from the summation of $nst$ nested variograms  $\\gamma_i(\\bf{h})$.\n",
    "\n",
    "The types of structure commonly applied include:\n",
    "\n",
    "* spherical\n",
    "\n",
    "* exponential\n",
    "\n",
    "* gaussian\n",
    "\n",
    "* nugget\n",
    "\n",
    "Other less common models include:\n",
    "\n",
    "* hole effect\n",
    "\n",
    "* dampenned hole effect\n",
    "\n",
    "* power law\n",
    "\n",
    "these will not be covered here.\n",
    "\n",
    "Each one of these variogram structures, $\\gamma_i(\\bf{h})$, is based on a geometric anisotropy model parameterized by the orientation and range in the major and minor directions.  In 2D this is simply an azimuth and ranges, $azi$, $a_{maj}$ and $a_{min}$. Note, the range in the minor direction (orthogonal to the major direction).\n",
    "\n",
    "The geometric anisotropy model assumes that the range in all off-diagonal directions is based on an ellipse with the major and minor axes alligned with and set to the major and minor for the variogram.\n",
    "\n",
    "\\begin{equation}\n",
    "\\bf{h}_i = \\sqrt{\\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2 + \\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2}  \n",
    "\\end{equation}\n",
    "\n",
    "Therefore, if we know the major direction, range in major and minor directions, we may completely describe each nested componnent of the complete spatial continuity of the variable of interest, $i = 1,\\dots,nst$.\n",
    "\n",
    "Some comments on modeling nested variograms:\n",
    "\n",
    "* we can capture nugget, short and long range continuity structures\n",
    "\n",
    "* we rely on the geometric anisotropy model, so all structures must inform the same level of contribution (porportion of the sill) in all directions.\n",
    "\n",
    "* the geometric anisotropy model is based on azimuth of the major direction of continuity, range in the major direction and range in the minor direction (orthogonal to the major direction).  The range is interpolated between the major and minor azimuths with a ellipse model\n",
    "\n",
    "* we can vary the type of variogram, direction or azimuth of the major direction, and major and minor ranges by structure\n",
    "\n",
    "In this workflow we will explore methods to:\n",
    "\n",
    "1. detect directionality from a spatial dataset\n",
    "2. calculate the directional variograms in the major and minor directions \n",
    "3. build a consistent 2D model fit to the major and minor directions\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from matplotlib import patches                          # draw variogram ellipse\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox\n",
    "import math\n",
    "from math import sin, cos, radians, pi\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vmodel_struct(nlag,xlag,azm,vario,istruct):\n",
    "    \n",
    "# Parameters\n",
    "    MAXNST=4\n",
    "    DEG2RAD=3.14159265/180.0 \n",
    "    MAXROT=MAXNST+1\n",
    "    EPSLON = 1.0e-20\n",
    "    VERSION= 1.01\n",
    "\n",
    "# Declare arrays\n",
    "    index = np.zeros(nlag+1)\n",
    "    h = np.zeros(nlag+1)\n",
    "    gam = np.zeros(nlag+1)\n",
    "    cov = np.zeros(nlag+1)\n",
    "    ro = np.zeros(nlag+1)\n",
    "    \n",
    "# Load the variogram\n",
    "    nst = vario[\"nst\"]; cc = np.zeros(nst); aa = np.zeros(nst); it = np.zeros(nst); ang = np.zeros(nst)\n",
    "    anis = np.zeros(nst)\n",
    "    \n",
    "    c0 = vario[\"nug\"]\n",
    "    cc[0] = vario[\"cc1\"]\n",
    "    it[0] = vario[\"it1\"]\n",
    "    ang[0] = vario[\"azi1\"]\n",
    "    aa[0] = vario[\"hmaj1\"]\n",
    "    anis[0] = vario[\"hmin1\"] / vario[\"hmaj1\"]\n",
    "    if nst == 2:\n",
    "        cc[1] = vario[\"cc2\"]\n",
    "        it[1] = vario[\"it2\"]\n",
    "        ang[1] = vario[\"azi2\"]\n",
    "        aa[1] = vario[\"hmaj2\"]\n",
    "        anis[1] = vario[\"hmin2\"] / vario[\"hmaj2\"]\n",
    "                    \n",
    "    xoff = math.sin(DEG2RAD*azm)*xlag\n",
    "    yoff = math.cos(DEG2RAD*azm)*xlag\n",
    "    print(' x,y,z offsets = ' + str(xoff) + ',' + str(yoff))\n",
    "    rotmat, maxcov = geostats.setup_rotmat(c0, nst, it, cc, ang, 99999.9)   \n",
    "          \n",
    "    \n",
    "    xx = 0.0; yy = 0.0      \n",
    "    for il in range(0,nlag+1):\n",
    "        index[il] = il\n",
    "        cov[il] = cova2_struct(0.0,0.0,xx,yy,nst,c0,9999.9,cc,aa,it,ang,anis,rotmat,maxcov,istruct)\n",
    "        #gam[il] = maxcov - cov[il]\n",
    "        if istruct == -1:\n",
    "            gam[il] = c0 - cov[il]  \n",
    "        else:\n",
    "            gam[il] = cc[istruct] - cov[il]\n",
    "        ro[il]  = cov[il]/maxcov\n",
    "        h[il]   = math.sqrt(max((xx*xx+yy*yy),0.0))\n",
    "        xx = xx + xoff\n",
    "        yy = yy + yoff\n",
    "\n",
    "    print(gam)\n",
    "# finished\n",
    "    return index,h,gam,cov,ro\n",
    "\n",
    "def cova2_struct(x1, y1, x2, y2, nst, c0, pmx, cc, aa, it, ang, anis, rotmat, maxcov, istruct):\n",
    "    EPSLON = 0.000001\n",
    "    # Check for very small distance\n",
    "    dx = x2 - x1\n",
    "    dy = y2 - y1\n",
    "\n",
    "    if (dx * dx + dy * dy) < EPSLON:\n",
    "        cova2_ = cc[istruct]\n",
    "        return cova2_\n",
    "    # Non-zero distance, loop over all the structures\n",
    "    cova2_ = 0.0\n",
    "    # Compute the appropriate structural distance \n",
    "    dx1 = dx * rotmat[0, istruct] + dy * rotmat[1, istruct]\n",
    "    dy1 = (dx * rotmat[2, istruct] + dy * rotmat[3, istruct]) / anis[istruct]\n",
    "    h = math.sqrt(max((dx1 * dx1 + dy1 * dy1), 0.0))\n",
    "    if istruct == -1:\n",
    "        return 0.0\n",
    "    elif istruct == 0 or istruct == 1:\n",
    "        if it[istruct] == 1:# Spherical model\n",
    "            hr = h / aa[istruct]\n",
    "            if hr < 1.0:\n",
    "                cova2_ = cova2_ + cc[istruct] * (1.0 - hr * (1.5 - 0.5 * hr * hr))\n",
    "        elif it[istruct] == 2: # Exponential model\n",
    "            cova2_ = cova2_ + cc[istruct] * np.exp(-3.0 * h / aa[istruct])\n",
    "        elif it[istruct] == 3: # Gaussian model\n",
    "            hh = -3.0 * (h * h) / (aa[istruct] * aa[istruct])\n",
    "            cova2_ = cova2_ + cc[istruct] * np.exp(hh)\n",
    "        elif it[istruct] == 4: # Power model\n",
    "            cov1 = pmx - cc[istruct] * (h ** aa[istruct])\n",
    "            cova2_ = cova2_ + cov1\n",
    "    return cova2_\n",
    "\n",
    "def point_pos(x0, y0, d, theta):\n",
    "    theta_rad = pi/2 - radians(theta)\n",
    "    return x0 + d*cos(theta_rad), y0 + d*sin(theta_rad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "2  50.0  800.0     1.0  23.435152   84.992437  2292.783358\n",
       "3  50.0  750.0     1.0  24.455309   90.632307  2494.848885\n",
       "4  50.0  700.0     1.0  23.178661  811.547979  2522.063995"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = 3\n",
    "\n",
    "if data == 1:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df['Porosity'] = df['Porosity']*100.0\n",
    "    df = df.iloc[:,1:]                                      # remove first column\n",
    "elif data == 2:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v3.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df = df.iloc[:,1:] \n",
    "elif data == 3:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/12_sample_data.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s) \n",
    "    df['Porosity'] = df['Porosity']*100.0\n",
    "    df = df.iloc[:,1:] \n",
    "elif data == 4:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v5_sand_only.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s) \n",
    "    df = df.iloc[:,1:] \n",
    "else:\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v1.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df = df.iloc[:,1:] \n",
    "    \n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features:\n",
    "\n",
    "* **X** - x coordinate in meters\n",
    "* **Y** - y coordinate in meters\n",
    "* **Porosity** - rock porosity averaged over a specific rock unit from a vertical well\n",
    "* **Perm** - rock permeability averaged (scaled up) over a specific rock unit from a vertical well \n",
    "* **AI** - acoustic impedance from a seismic cube assigned at a specific rock unit and at the location of a vertical well \n",
    "* **facies** - facies, 0 - shale, 1 - sandstone\n",
    "\n",
    "Concerning facies:\n",
    "\n",
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>480.0</td>\n",
       "      <td>430.187500</td>\n",
       "      <td>263.832692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>480.0</td>\n",
       "      <td>522.166667</td>\n",
       "      <td>284.293420</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>480.0</td>\n",
       "      <td>0.616667</td>\n",
       "      <td>0.486706</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>480.0</td>\n",
       "      <td>18.943989</td>\n",
       "      <td>3.170164</td>\n",
       "      <td>11.756196</td>\n",
       "      <td>16.588395</td>\n",
       "      <td>18.544339</td>\n",
       "      <td>21.651266</td>\n",
       "      <td>26.109068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>480.0</td>\n",
       "      <td>520.932093</td>\n",
       "      <td>1226.207190</td>\n",
       "      <td>0.005776</td>\n",
       "      <td>6.539988</td>\n",
       "      <td>49.451463</td>\n",
       "      <td>369.470756</td>\n",
       "      <td>10319.904849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>480.0</td>\n",
       "      <td>3758.879653</td>\n",
       "      <td>779.990582</td>\n",
       "      <td>1746.387548</td>\n",
       "      <td>3212.900121</td>\n",
       "      <td>3719.883000</td>\n",
       "      <td>4236.160395</td>\n",
       "      <td>6194.573653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean          std          min          25%  \\\n",
       "X         480.0   430.187500   263.832692     0.000000   200.000000   \n",
       "Y         480.0   522.166667   284.293420    19.000000   279.000000   \n",
       "Facies    480.0     0.616667     0.486706     0.000000     0.000000   \n",
       "Porosity  480.0    18.943989     3.170164    11.756196    16.588395   \n",
       "Perm      480.0   520.932093  1226.207190     0.005776     6.539988   \n",
       "AI        480.0  3758.879653   779.990582  1746.387548  3212.900121   \n",
       "\n",
       "                  50%          75%           max  \n",
       "X          390.000000   630.000000    980.000000  \n",
       "Y          539.000000   759.000000    999.000000  \n",
       "Facies       1.000000     1.000000      1.000000  \n",
       "Porosity    18.544339    21.651266     26.109068  \n",
       "Perm        49.451463   369.470756  10319.904849  \n",
       "AI        3719.883000  4236.160395   6194.573653  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                               # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Model Parameters\n",
    "\n",
    "See the the following model parameters:\n",
    "\n",
    "* **xmin**, **xmax**, **ymin** and **ymax** - extents of the dataset for plotting\n",
    "* **feature** and **feature_units** - feature of interest and associated units\n",
    "* **vmin** and **vmax** - minimum and maximum of the feature of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0                                # spatial extents in x and y\n",
    "ymin = 0.0; ymax = 1000.0\n",
    "feature = 'Porosity'; feature_units = 'percentage'         # name and units of the feature of interest\n",
    "vmin = 0.0; vmax = 25.0                                  # min and max of the feature of interest\n",
    "cmap = plt.cm.inferno                                    # set the color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the feature to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['N' + feature], tvPor, tnsPor = geostats.nscore(df, feature) # nscore transform for all facies porosity "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPorosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.076146</td>\n",
       "      <td>140.021266</td>\n",
       "      <td>3413.063944</td>\n",
       "      <td>0.808592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.715447</td>\n",
       "      <td>39.837129</td>\n",
       "      <td>3074.562617</td>\n",
       "      <td>1.403672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.435152</td>\n",
       "      <td>84.992437</td>\n",
       "      <td>2292.783358</td>\n",
       "      <td>1.324260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.455309</td>\n",
       "      <td>90.632307</td>\n",
       "      <td>2494.848885</td>\n",
       "      <td>1.720087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.178661</td>\n",
       "      <td>811.547979</td>\n",
       "      <td>2522.063995</td>\n",
       "      <td>1.155424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      X      Y  Facies   Porosity        Perm           AI  NPorosity\n",
       "0  50.0  900.0     1.0  22.076146  140.021266  3413.063944   0.808592\n",
       "1  50.0  850.0     1.0  23.715447   39.837129  3074.562617   1.403672\n",
       "2  50.0  800.0     1.0  23.435152   84.992437  2292.783358   1.324260\n",
       "3  50.0  750.0     1.0  24.455309   90.632307  2494.848885   1.720087\n",
       "4  50.0  700.0     1.0  23.178661  811.547979  2522.063995   1.155424"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df[feature], facecolor='red',bins=np.linspace(vmin,vmax,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black')\n",
    "plt.xlim([vmin,vmax]); plt.ylim([0,1.0])\n",
    "plt.xlabel(feature + '(' + feature_units + ')'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)  \n",
    "plt.hist(df['N'+feature], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Gaussian Transformed ' + feature); plt.ylabel('Frequency'); plt.title('Guassian Transformed ' + feature)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the normal score transform has correctly transformed the feature to standard normal.\n",
    "\n",
    "#### Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the variogram calculation search template.\n",
    "\n",
    "Let's plot the location maps of the original feature and the normal score transforms of the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y',feature,0,1000,0,1000,vmin,vmax,feature,'X (m)','Y (m)',feature,cmap)\n",
    "\n",
    "plt.subplot(122)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y','N'+feature,0,1000,0,1000,-3,3,'Gaussian Transformed ' + feature,'X (m)','Y (m)','Gaussian Transformed ' + feature,cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit!\n",
    "\n",
    "#### Experimental Variograms\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters. For example:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n",
    "\n",
    "#### Dashboard for Interactive Variogram Calculation and Modeling\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating and modeling experimental variograms.\n",
    "\n",
    "* allowing you to calculate and model the variogram of the normal score transformed variogram interactively while changing (and exploring) the search template parameters.\n",
    "\n",
    "* first calculate the isotropic or directional variogram(s)\n",
    "\n",
    "* then fit the same isotropic or directional variogram(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the experimental variogram\n",
    "l = widgets.Text(value='                              Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "lag = widgets.FloatSlider(min = 10, max = 500, value = 10, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag.style.handle_color = 'gray'\n",
    "\n",
    "lag_tol = widgets.FloatSlider(min = 5, max = 500, value = 5, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag_tol.style.handle_color = 'gray'\n",
    "\n",
    "nlag = widgets.IntSlider(min = 1, max = 100, value = 100, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "nlag.style.handle_color = 'gray'\n",
    "\n",
    "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi.style.handle_color = 'gray'\n",
    "\n",
    "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 10, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "\n",
    "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting    \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth):     # function to take parameters, calculate variogram and plot\n",
    "#    text_trap = io.StringIO()\n",
    "#    sys.stdout = text_trap\n",
    "    global lags,gammas,npps,lags2,gammas2,npps2\n",
    "    tmin = -9999.9; tmax = 9999.9\n",
    "    lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",\"N\"+feature,tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=1.0)\n",
    "    lags2, gammas2, npps2 = geostats.gamv(df,\"X\",\"Y\",\"N\"+feature,tmin,tmax,lag,lag_tol,nlag,azi+90.0,azi_tol,bandwidth,isill=1.0)\n",
    "    \n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi), alpha = 0.8)\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi+90.0), alpha = 0.8)\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol < 90.0:\n",
    "        plt.title('Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(np.round(azi,0)) + ', Azi. Tol.' + str(azi_tol))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.0, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "    return\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Variogram Calculation Demonstration\n",
    "\n",
    "* calculate omnidirectional and direction experimental variograms \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data. Note, size of the experimental point is scaled by the number of pairs.\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance\n",
    "\n",
    "* **number of lags** - number of lags in the experimental variogram\n",
    "\n",
    "* **bandwidth** - maximum departure from the lag vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0506d78df31d4b94a40385aede4ee476",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Calculation Interactive Demonstration, Mich…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c90f38bee07c4757be140bc5d6a5fd50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
    "l = widgets.Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "nug = widgets.FloatSlider(min = 0, max = 1.0, value = 0.0, step = 0.1, description = r'$c_{nugget}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "nug.style.handle_color = 'gray'\n",
    "it1 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description=r'$Type_1$:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c1 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.1, description = r'$c_1$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "c1.style.handle_color = 'gray'\n",
    "hmaj1 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 25.0, description = r'$a_{1,maj}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmaj1.style.handle_color = 'black'\n",
    "hmin1 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 25.0, description = r'$a_{1,min}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmin1.style.handle_color = 'red'\n",
    "\n",
    "it2 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description=r'$Type_2$:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c2 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.0, description = r'$c_2$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "c2.style.handle_color = 'gray'\n",
    "hmaj2 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 100.0, description = r'$a_{2,maj}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmaj2.style.handle_color = 'black'\n",
    "hmin2 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 100.0, description = r'$a_{2,min}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmin2.style.handle_color = 'red'\n",
    "\n",
    "ui1 = widgets.HBox([nug,it1,c1,hmaj1,hmin1,it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "#ui2 = widgets.HBox([it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def convert_type(it):\n",
    "    if it == 'Spherical': \n",
    "        return 1\n",
    "    elif it == 'Exponential':\n",
    "        return 2\n",
    "    else: \n",
    "        return 3\n",
    "\n",
    "def f_make(nug,it1,c1, hmaj1, hmin1, it2, c2, hmaj2, hmin2):                       # function to take parameters, make sample and plot\n",
    "    text_trap = io.StringIO()\n",
    "    sys.stdout = text_trap\n",
    "    \n",
    "    it1 = convert_type(it1); it2 = convert_type(it2)\n",
    "    if c2 > 0.0:\n",
    "        nst = 2\n",
    "    else:\n",
    "        nst = 1\n",
    "    \n",
    "    vario = GSLIB.make_variogram(nug,nst,it1,c1,0.0,hmaj1,hmin1,it2,c2,0.0,hmaj2,hmin2) # make model object\n",
    "    nlag = 100; xlag = 10;                                     \n",
    "    index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,0.0,vario)   # project the model in the major azimuth                                                  # project the model in the 135 azimuth\n",
    "    index_min,h_min,gam_min,cov_min,ro_min = geostats.vmodel(nlag,xlag,90.0,vario)   \n",
    "\n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi.value), alpha = 0.8)\n",
    "    plt.plot(h_maj,gam_maj,color = 'black')\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi.value+90.0), alpha = 0.8)\n",
    "    plt.plot(h_min,gam_min,color = 'red')\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol.value < 90.0:\n",
    "        plt.title('Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azi.value) + ', Azi. Tol.' + str(azi_tol.value))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.5, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'nug':nug, 'it1':it1,'c1':c1, 'hmaj1':hmaj1, 'hmin1':hmin1, 'it2':it2, 'c2':c2, 'hmaj2':hmaj2, 'hmin2':hmin2})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Nested Variogram Modeling Demostration\n",
    "\n",
    "* select the nested structures and their types, contributions and major and minor ranges \n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Fit a positive definite variogram model based on the addition of multiple structures each describing spatial components of the feature variance \n",
    "\n",
    "* **nug**: nugget effect\n",
    "\n",
    "* **c1 / c2**: contributions of the sill\n",
    "\n",
    "* **hmaj1 / hmaj2**: range in the major direction\n",
    "\n",
    "* **hmin1 / hmin2**: range in the minor direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abdfcc4804da47d991ef9e45aa17b9be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd5a22a0fba34d19923fe913fec99ea3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
    "l = widgets.Text(value='               Variogram Modeling, Visualize Nested Structures and Azimuth Interpolation, Michael Pyrcz, Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "nug = widgets.FloatSlider(min = 0.0001, max = 1.0, value = 0.0001, step = 0.1, description = r'$c_{nugget}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "nug.style.handle_color = 'gray'\n",
    "it1 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description=r'$Type_1$:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "# c1 = widgets.FloatSlider(min=0.0001, max = 1.0, value = 0.2, description = r'$c_1$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "# c1.style.handle_color = 'gray'\n",
    "hmaj1 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 800.0, step = 25.0, description = r'$a_{1,maj}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmaj1.style.handle_color = 'black'\n",
    "hmin1 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 325.0, step = 25.0, description = r'$a_{1,min}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmin1.style.handle_color = 'red'\n",
    "\n",
    "it2 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description=r'$Type_2$:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c2 = widgets.FloatSlider(min=0.0001, max = 1.0, value = 0.0001, description = r'$c_2$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "c2.style.handle_color = 'gray'\n",
    "hmaj2 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 800.0, step = 25.0, description = r'$a_{2,maj}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmaj2.style.handle_color = 'black'\n",
    "hmin2 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 325.0, step = 25.0, description = r'$a_{2,min}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmin2.style.handle_color = 'red'\n",
    "new_azimuth = widgets.FloatSlider(min = 0.0, max = 360.0, value = 45.0, step = 5.0, description = r'$azi_{inter}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "new_azimuth.style.handle_color = 'purple'\n",
    "\n",
    "ui9 = widgets.HBox([nug,it1,hmaj1,hmin1,it2,c2,hmaj2,hmin2,new_azimuth],)                   # basic widget formatting   \n",
    "#ui2 = widgets.HBox([it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "ui10 = widgets.VBox([l,ui9],)\n",
    "\n",
    "def convert_type(it):\n",
    "    if it == 'Spherical': \n",
    "        return 1\n",
    "    elif it == 'Exponential':\n",
    "        return 2\n",
    "    else: \n",
    "        return 3\n",
    "\n",
    "def f_make2(nug,it1,hmaj1,hmin1,it2,c2,hmaj2,hmin2,new_azimuth):                       # function to take parameters, make sample and plot\n",
    "    azimuth = azi.value\n",
    "    c1 = 1.0 - nug - c2\n",
    "    it1 = convert_type(it1); it2 = convert_type(it2)\n",
    "    if c2 > 0.0:\n",
    "        nst = 2\n",
    "    else:\n",
    "        nst = 1\n",
    "    \n",
    "    vario = GSLIB.make_variogram(nug,nst,it1,c1,0.0,hmaj1,hmin1,it2,c2,0.0,hmaj2,hmin2) # make model object\n",
    "    nlag = 100; xlag = 10;                                     \n",
    "    index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,0.0,vario)   # project the model in the major azimuth                                                  # project the model in the 135 azimuth\n",
    "    index_min,h_min,gam_min,cov_min,ro_min = geostats.vmodel(nlag,xlag,90.0,vario)\n",
    "    index_new,h_new,gam_new,cov_new,ro_new = geostats.vmodel(nlag,xlag,azimuth-new_azimuth,vario)\n",
    "    \n",
    "    _,h_maj0,gam_maj0,_,_ = vmodel_struct(nlag,xlag,0.0,vario,-1) \n",
    "    _,h_maj1,gam_maj1,_,_ = vmodel_struct(nlag,xlag,0.0,vario,0) \n",
    "    _,h_maj2,gam_maj2,_,_ = vmodel_struct(nlag,xlag,0.0,vario,1) \n",
    "    _,h_min0,gam_min0,_,_ = vmodel_struct(nlag,xlag,90.0,vario,-1) \n",
    "    _,h_min1,gam_min1,_,_ = vmodel_struct(nlag,xlag,90.0,vario,0) \n",
    "    _,h_min2,gam_min2,_,_ = vmodel_struct(nlag,xlag,90.0,vario,1) \n",
    "    _,h_new0,gam_new0,_,_ = vmodel_struct(nlag,xlag,azimuth-new_azimuth,vario,-1) \n",
    "    _,h_new1,gam_new1,_,_ = vmodel_struct(nlag,xlag,azimuth-new_azimuth,vario,0) \n",
    "    _,h_new2,gam_new2,_,_ = vmodel_struct(nlag,xlag,azimuth-new_azimuth,vario,1) \n",
    "    \n",
    "    plt.subplot(221)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azimuth), alpha = 0.8,zorder=10)\n",
    "    plt.plot(h_maj,gam_maj,color = 'black',lw=3,zorder=10)\n",
    "    if nug > 0.0001: \n",
    "        plt.plot(h_maj0,gam_maj0,color = 'black',lw=1.5)\n",
    "        plt.fill_between(h_maj,gam_maj0,np.full(len(h_maj),0),color='grey',alpha=1.0,zorder=1,label = 'Nugget')\n",
    "    if c1 > 0.0001: \n",
    "        plt.plot(h_maj,gam_maj1+gam_maj0,color = 'black',lw=1.5)\n",
    "        plt.fill_between(h_maj,gam_maj1+gam_maj0,gam_maj0,color='darkorange',alpha=1.0,zorder=1,label = 'Structure #1')\n",
    "    \n",
    "    if c2 > 0.0001:   \n",
    "        plt.plot(h_maj,gam_maj2+gam_maj1+gam_maj0,color = 'black',lw=1.5)\n",
    "        plt.fill_between(h_maj,gam_maj2+gam_maj1+gam_maj0,gam_maj1+gam_maj0,color='deepskyblue',alpha=1.0,zorder=1,label='Structure #2')\n",
    "    \n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black',ls='--')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)'); plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol.value < 90.0:\n",
    "        plt.title('Major Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azimuth))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "\n",
    "    if c1 > 0.0001:\n",
    "        plt.vlines(hmaj1,0,1.8,color='black',lw=1.5); \n",
    "        plt.annotate('Structure 1 Range',[hmaj1-30,1.3],rotation=90.0);\n",
    "    if c2 > 0.0001:\n",
    "        plt.vlines(hmaj2,0,1.8,color='black',lw=2.0)\n",
    "        plt.annotate('Structure 2 Range',[hmaj2-30,1.3],color='black',rotation=90.0)\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplot(222)                                    # plot experimental variogram\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azimuth+90.0), alpha = 0.8,zorder=10)\n",
    "    plt.plot(h_min,gam_min,color = 'red',lw=3)\n",
    "    if nug > 0.0001:\n",
    "        plt.plot(h_min0,gam_min0,color = 'red',lw=1.5)\n",
    "        plt.fill_between(h_min,gam_min0,np.full(len(h_maj),0),color='grey',alpha=1.0,zorder=1,label = 'Nugget')\n",
    "    if c1 > 0.0001:  \n",
    "        plt.plot(h_min,gam_min1+gam_min0,color = 'red',lw=1.5)\n",
    "        plt.fill_between(h_min,gam_min1+gam_min0,gam_min0,color='darkorange',alpha=1.0,zorder=1,label = 'Structure #1')\n",
    "    \n",
    "    if c2 > 0.0001:   \n",
    "        plt.plot(h_min,gam_min2+gam_min1+gam_min0,color = 'red',lw=1.5)\n",
    "        plt.fill_between(h_min,gam_min2+gam_min1+gam_min0,gam_min1+gam_min0,color='deepskyblue',alpha=1.0,zorder=1,label='Structure #2')\n",
    "    \n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black',ls='--')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol.value < 90.0:\n",
    "        plt.title('Minor Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azimuth + 90.0))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "    if c1 > 0.0001:      \n",
    "        plt.vlines(hmin1,0,1.8,color='red',lw=1.5)\n",
    "        plt.annotate('Structure 1 Range',[hmin1-30,1.3],color='red',rotation=90.0)\n",
    "    if c2 > 0.0001:  \n",
    "        plt.vlines(hmin2,0,1.8,color='red',lw=2.0)\n",
    "        plt.annotate('Structure 2 Range',[hmin2-30,1.3],color='red',rotation=90.0)\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplot(223)                                    # plot experimental variogram\n",
    "    plt.plot(h_new,gam_new,color = 'purple',lw=3)\n",
    "    if nug > 0.0001:\n",
    "        plt.plot(h_new0,gam_new0,color = 'purple',lw=1.5)\n",
    "        plt.fill_between(h_new,gam_new0,np.full(len(h_maj),0),color='grey',alpha=1.0,zorder=1,label = 'Nugget')\n",
    "    if c1 > 0.0001:\n",
    "        plt.plot(h_new,gam_new1+gam_new0,color = 'purple',lw=1.5)\n",
    "        plt.fill_between(h_new,gam_new1+gam_new0,gam_new0,color='darkorange',alpha=1.0,zorder=1,label = 'Structure #1')\n",
    "    \n",
    "    if c2 > 0.0001:   \n",
    "        plt.plot(h_new,gam_new2+gam_new1+gam_new0,color = 'purple',lw=1.5)\n",
    "        plt.fill_between(h_new,gam_new2+gam_new1+gam_new0,gam_new1+gam_new0,color='deepskyblue',alpha=1.0,zorder=1,label='Structure #2')\n",
    "    \n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black',ls='--')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol.value < 90.0:\n",
    "        plt.title('Interpolated ' + feature + ' Variogram - Azi. ' + str(new_azimuth))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "        \n",
    "    if c1 > 0.0001:\n",
    "        plt.vlines(hmaj1,0,1.8,color='black',lw=1.5); \n",
    "\n",
    "    if c1 > 0.0001:      \n",
    "        plt.vlines(hmin1,0,1.8,color='red',lw=1.5)\n",
    "        \n",
    "    if c2 > 0.0001:\n",
    "        plt.vlines(hmaj2,0,1.8,color='black',lw=1.5); \n",
    "  \n",
    "    if c2 > 0.0001:      \n",
    "        plt.vlines(hmin2,0,1.8,color='red',lw=1.5)\n",
    "    \n",
    "    # plt.vlines(hmin1,0,1.8,color='black',lw=1.5); plt.vlines(hmin2,0,1.8,color='deepskyblue',lw=1.5)\n",
    "    # plt.annotate('Structure 1 Range',[hmin1-30,1.3],rotation=90.0); plt.annotate('Structure 2 Range',[hmin2-30,1.3],color='deepskyblue',rotation=90.0)\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplot(224)\n",
    "    \n",
    "    plt.xlim([-2000,2000]); plt.ylim([-2000,2000]); plt.xlabel('X offset (m)'); plt.ylabel('Y offset (m)')\n",
    "    plt.plot([-2000,2000],[0,0],color='grey',lw=3,zorder=1); plt.plot([0,0],[-2000,2000],color='grey',lw=3,zorder=1)\n",
    "    plt.grid(True); plt.title('2D Variogram Structures - Ranges and Geometric Anisotropy')\n",
    "    \n",
    "    if c1 > 0.0001:\n",
    "        e1 = patches.Ellipse((0, 0), hmaj1*2, hmin1*2,angle=90-azimuth, linewidth=2, fill=True,facecolor='darkorange',edgecolor='black',alpha=1.0,zorder=4)\n",
    "        plt.gca().add_patch(e1)\n",
    "    \n",
    "    if c2 > 0.0001:\n",
    "        e2 = patches.Ellipse((0, 0), hmaj2*2, hmin2*2,angle=90-azimuth, linewidth=2, fill=True,facecolor='deepskyblue',edgecolor='black',alpha=1.0,zorder=2)\n",
    "        plt.gca().add_patch(e2)\n",
    "    \n",
    "    xarr,yarr = point_pos(0, 0, hmaj1, azimuth)\n",
    "    plt.plot([0,xarr],[0,yarr],color='black',zorder=13)\n",
    "    \n",
    "    xarr2,yarr2 = point_pos(0, 0, hmaj2, azimuth)\n",
    "    plt.plot([0,xarr2],[0,yarr2],color='black',zorder=10)\n",
    "    plt.annotate('Major',[xarr2,yarr2],zorder=10)\n",
    "    \n",
    "    xarr1,yarr1 = point_pos(0, 0, hmin1, azimuth+90.0)\n",
    "    plt.plot([0,xarr1],[0,yarr1],color='red',zorder=13)\n",
    "    \n",
    "    xarr2,yarr2 = point_pos(0, 0, hmin2, azimuth+90.0)\n",
    "    plt.plot([0,xarr2],[0,yarr2],color='red',zorder=10)\n",
    "    plt.annotate('Minor',[xarr2,yarr2],zorder=10,color='red')\n",
    "    \n",
    "    xarr_new,yarr_new = point_pos(0, 0, 1200, new_azimuth)\n",
    "    plt.plot([0,xarr_new],[0,yarr_new],color='purple',zorder=10,lw=3)\n",
    "    plt.annotate('Interpolated Azimuth',[xarr_new,yarr_new],color='purple',zorder=10)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.5, wspace=0.2, hspace=0.2)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot2 = widgets.interactive_output(f_make2, {'nug':nug, 'it1':it1, 'hmaj1':hmaj1, 'hmin1':hmin1, 'it2':it2, 'c2':c2, 'hmaj2':hmaj2, 'hmin2':hmin2,'new_azimuth':new_azimuth})\n",
    "interactive_plot2.clear_output(wait = True)               # reduce flickering by delaying plot updating  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Nested Variogram Modeling Demostration - Nested Structures and Azimuth Interpolation\n",
    "\n",
    "* select the nested structures and their types, contributions and major and minor ranges, visualize structures and azimuth interpolated variogram \n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Fit a positive definite variogram model based on the addition of multiple structures each describing spatial components of the feature variance \n",
    "\n",
    "* **nug**: nugget effect\n",
    "\n",
    "* **c1 / c2**: contributions of the sill - note, **c1** is set at 1.0 - **nug** - **c2**\n",
    "\n",
    "* **hmaj1 / hmaj2**: range in the major direction\n",
    "\n",
    "* **hmin1 / hmin2**: range in the minor direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "VBox(children=(Text(value='               Variogram Modeling, Visualize Nested Structures and Azimuth Interpol…"
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui10, interactive_plot2)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration / exercise of variogram calculation and modeling for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Professor, Cockrell School of Engineering and The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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